{"id":"W2513021552","doi":"10.1002/dac.3180","title":"An efficient network‐coding based back‐pressure scheduling algorithm for wireless multi‐hop networks","year":2016,"lang":"en","type":"article","venue":"International Journal of Communication Systems","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"National Natural Science Foundation of China","keywords":"Computer science; Linear network coding; Computer network; Hop (telecommunications); Wireless network; Coding (social sciences); Wireless; Scheduling (production processes); Maximum throughput scheduling; Distributed computing; Algorithm; Round-robin scheduling; Fair-share scheduling; Telecommunications; Quality of service; Mathematical optimization; Network packet","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008692789,0.0001939111,0.0003050341,0.0001806919,0.000108309,0.0001395279,0.001045949,0.00013596,0.00001454781],"category_scores_gemma":[0.00004202589,0.0001611022,0.0001264582,0.000155875,0.00004722658,0.0004568865,0.00004547351,0.0002093509,0.000004779613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002112306,"about_ca_system_score_gemma":0.0000391989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000310651,"about_ca_topic_score_gemma":0.000002824337,"domain_scores_codex":[0.9981351,0.0001813528,0.0008895668,0.0001378076,0.0004020795,0.0002540982],"domain_scores_gemma":[0.997045,0.0005468858,0.0006030847,0.0004436187,0.001241729,0.0001196313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003544016,0.00004701166,0.0001786921,0.000006445058,0.0001605702,0.000001267493,0.00005213781,0.9664105,0.0004110001,0.0005671295,0.000186139,0.03194363],"study_design_scores_gemma":[0.001588875,0.00003236262,0.00007569268,0.0008875496,0.00002861125,0.00001929819,0.00006249869,0.9927774,0.0001931435,0.0000181909,0.004125962,0.0001904277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001945197,0.003152913,0.9920349,0.0001446428,0.002264741,0.0002882951,0.00002378445,0.00008887736,0.00005670662],"genre_scores_gemma":[0.8426331,0.0007017225,0.1555853,0.00003005542,0.0009019832,0.00002982237,0.00003299005,0.00005731766,0.00002773112],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8406879,"threshold_uncertainty_score":0.6569558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01701329410286068,"score_gpt":0.2750245534377791,"score_spread":0.2580112593349184,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}